


"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import jax
 import jaxlib

UpperCAmelCase				=						get_logger()

UpperCAmelCase				=						None




class 	lowercase__				(					TensorFormatter[Mapping, '''jax.Array''', Mapping]				):





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None      ,   **SCREAMING_SNAKE_CASE)  ->  int:
      super().__init__(features=SCREAMING_SNAKE_CASE)
      import jax
      from jaxlib.xla_client import Device

      if isinstance(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE):
         raise ValueError(
             F'Expected {device} to be a `str` not {type(SCREAMING_SNAKE_CASE)}, as `jaxlib.xla_extension.Device` '
             """is not serializable neither with `pickle` nor with `dill`. Instead you can surround """
             """the device with `str()` to get its string identifier that will be internally mapped """
             """to the actual `jaxlib.xla_extension.Device`.""")
      _lowerCamelCase	:					str   =		device if isinstance(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE) else str(jax.devices()[0])
      # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
      # with `pickle` nor with `dill`, so we need to use a global variable instead
      global DEVICE_MAPPING
      if DEVICE_MAPPING is None:
         _lowerCamelCase	:					Optional[int]   =		self._map_devices_to_str()
      if self.device not in list(DEVICE_MAPPING.keys()):
         logger.warning(
             F'Device with string identifier {self.device} not listed among the available '
             F'devices: {list(DEVICE_MAPPING.keys())}, so falling back to the default '
             F'device: {str(jax.devices()[0])}.')
         _lowerCamelCase	:					str   =		str(jax.devices()[0])
      _lowerCamelCase	:					Tuple   =		jnp_array_kwargs





   @staticmethod
   def 		UpperCamelCase_  (							)  ->  Dict[str, "jaxlib.xla_extension.Device"]:
      import jax

      return {str(SCREAMING_SNAKE_CASE): device for device in jax.devices()}





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      import jax
      import jax.numpy as jnp

      if isinstance(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE) and column:
         if all(
             isinstance(SCREAMING_SNAKE_CASE      ,   jax.Array) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column):
            return jnp.stack(SCREAMING_SNAKE_CASE      ,   axis=0)
      return column





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      import jax
      import jax.numpy as jnp

      if isinstance(SCREAMING_SNAKE_CASE      ,   (str, bytes, type(SCREAMING_SNAKE_CASE))):
         return value
      elif isinstance(SCREAMING_SNAKE_CASE      ,   (np.character, np.ndarray)) and np.issubdtype(value.dtype      ,   np.character):
         return value.tolist()

      _lowerCamelCase	:					List[str]   =		{}

      if isinstance(SCREAMING_SNAKE_CASE      ,   (np.number, np.ndarray)) and np.issubdtype(value.dtype      ,   np.integer):
         # the default int precision depends on the jax config
         # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
         if jax.config.jax_enable_xaa:
            _lowerCamelCase	:					int   =		{"""dtype""": jnp.intaa}
         else:
            _lowerCamelCase	:					Dict   =		{"""dtype""": jnp.intaa}
      elif isinstance(SCREAMING_SNAKE_CASE      ,   (np.number, np.ndarray)) and np.issubdtype(value.dtype      ,   np.floating):
         _lowerCamelCase	:					Any   =		{"""dtype""": jnp.floataa}
      elif config.PIL_AVAILABLE and "PIL" in sys.modules:
         import PIL.Image

         if isinstance(SCREAMING_SNAKE_CASE      ,   PIL.Image.Image):
            _lowerCamelCase	:					List[Any]   =		np.asarray(SCREAMING_SNAKE_CASE)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
      global DEVICE_MAPPING
      if DEVICE_MAPPING is None:
         _lowerCamelCase	:					List[Any]   =		self._map_devices_to_str()

      with jax.default_device(DEVICE_MAPPING[self.device]):
         # calling jnp.array on a np.ndarray does copy the data
         # see https://github.com/google/jax/issues/4486
         return jnp.array(SCREAMING_SNAKE_CASE      ,   **{**default_dtype, **self.jnp_array_kwargs})





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Any:
      import jax

      # support for torch, tf, jax etc.
      if config.TORCH_AVAILABLE and "torch" in sys.modules:
         import torch

         if isinstance(SCREAMING_SNAKE_CASE      ,   torch.Tensor):
            return self._tensorize(data_struct.detach().cpu().numpy()[()])
      if hasattr(SCREAMING_SNAKE_CASE      ,   """__array__""") and not isinstance(SCREAMING_SNAKE_CASE      ,   jax.Array):
         _lowerCamelCase	:					str   =		data_struct.__array__()
      # support for nested types like struct of list of struct
      if isinstance(SCREAMING_SNAKE_CASE      ,   np.ndarray):
         if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
            return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE) for substruct in data_struct])
      elif isinstance(SCREAMING_SNAKE_CASE      ,   (list, tuple)):
         return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE) for substruct in data_struct])
      return self._tensorize(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      return map_nested(self._recursive_tensorize      ,   SCREAMING_SNAKE_CASE      ,   map_list=SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Mapping:
      _lowerCamelCase	:					List[str]   =		self.numpy_arrow_extractor().extract_row(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[str]   =		self.python_features_decoder.decode_row(SCREAMING_SNAKE_CASE)
      return self.recursive_tensorize(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  "jax.Array":
      _lowerCamelCase	:					str   =		self.numpy_arrow_extractor().extract_column(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					int   =		self.python_features_decoder.decode_column(SCREAMING_SNAKE_CASE      ,   pa_table.column_names[0])
      _lowerCamelCase	:					List[str]   =		self.recursive_tensorize(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Any   =		self._consolidate(SCREAMING_SNAKE_CASE)
      return column





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Mapping:
      _lowerCamelCase	:					Optional[int]   =		self.numpy_arrow_extractor().extract_batch(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		self.python_features_decoder.decode_batch(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		self.recursive_tensorize(SCREAMING_SNAKE_CASE)
      for column_name in batch:
         _lowerCamelCase	:					int   =		self._consolidate(batch[column_name])
      return batch


import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
		raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
		raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
__a    :	Any         =    logging.get_logger(__name__)

__a    :	Union[str, Any]         =    """Hello, World!"""
__a    :	Optional[int]         =    """en_XX"""





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				Path('''data_bin'''				)
				__lowercase					     =				FairseqXmodModel.from_pretrained(
				    model_name_or_path=str(Path(lowercase				).parent				)				,     checkpoint_file=Path(lowercase				).name				,     _name='''xmod_base'''				,     arch='''xmod_base'''				,     task='''multilingual_masked_lm'''				,     data_name_or_path=str(lowercase				)				,     bpe='''sentencepiece'''				,     sentencepiece_model=str(Path(lowercase				).parent / '''sentencepiece.bpe.model'''				)				,     src_dict=str(data_dir / '''dict.txt'''				)				,     )
				xmod.eval()  # disable dropout
				print(lowercase				)

				__lowercase					     =				xmod.model.encoder.sentence_encoder
				__lowercase					     =				XmodConfig(
				    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings				,     hidden_size=xmod.cfg.model.encoder_embed_dim				,     num_hidden_layers=xmod.cfg.model.encoder_layers				,     num_attention_heads=xmod.cfg.model.encoder_attention_heads				,     intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim				,     max_position_embeddings=514				,     type_vocab_size=1				,     layer_norm_eps=1E-5				,     pre_norm=xmod.cfg.model.encoder_normalize_before				,     adapter_reduction_factor=getattr(xmod.cfg.model				,     '''bottleneck'''				,     2				)				,     adapter_layer_norm=xmod.cfg.model.adapter_layer_norm				,     adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm				,     ln_before_adapter=xmod.cfg.model.ln_before_adapter				,     languages=xmod.cfg.model.languages				,     )
				if classification_head:
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

				print('''Our X-MOD config:'''				,     lowercase				)

				__lowercase					     =				XmodForSequenceClassification(lowercase				) if classification_head else XmodForMaskedLM(lowercase				)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				__lowercase					     =				xmod_sent_encoder.embed_tokens.weight
				__lowercase					     =				xmod_sent_encoder.embed_positions.weight
				__lowercase					     =				torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c xmod doesn't use them.

				__lowercase					     =				xmod_sent_encoder.layernorm_embedding.weight
				__lowercase					     =				xmod_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers				):
								# Encoder: start of layer
								__lowercase					     =				model.roberta.encoder.layer[i]
								__lowercase					     =				xmod_sent_encoder.layers[i]

								# self attention
								__lowercase					     =				layer.attention.self
								if not (
								    xmod_layer.self_attn.k_proj.weight.data.shape
								    == xmod_layer.self_attn.q_proj.weight.data.shape
								    == xmod_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)				)
								):
												raise AssertionError('''Dimensions of self-attention weights do not match.'''				)

								__lowercase					     =				xmod_layer.self_attn.q_proj.weight
								__lowercase					     =				xmod_layer.self_attn.q_proj.bias
								__lowercase					     =				xmod_layer.self_attn.k_proj.weight
								__lowercase					     =				xmod_layer.self_attn.k_proj.bias
								__lowercase					     =				xmod_layer.self_attn.v_proj.weight
								__lowercase					     =				xmod_layer.self_attn.v_proj.bias

								# self-attention output
								__lowercase					     =				layer.attention.output
								if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
												raise AssertionError('''Dimensions of self-attention output weights do not match.'''				)
								__lowercase					     =				xmod_layer.self_attn.out_proj.weight
								__lowercase					     =				xmod_layer.self_attn.out_proj.bias
								__lowercase					     =				xmod_layer.self_attn_layer_norm.weight
								__lowercase					     =				xmod_layer.self_attn_layer_norm.bias

								# intermediate
								__lowercase					     =				layer.intermediate
								if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError('''Dimensions of intermediate weights do not match.'''				)
								__lowercase					     =				xmod_layer.fca.weight
								__lowercase					     =				xmod_layer.fca.bias

								# output
								__lowercase					     =				layer.output
								if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError('''Dimensions of feed-forward weights do not match.'''				)
								__lowercase					     =				xmod_layer.fca.weight
								__lowercase					     =				xmod_layer.fca.bias
								__lowercase					     =				xmod_layer.final_layer_norm.weight
								__lowercase					     =				xmod_layer.final_layer_norm.bias
								if bert_output.adapter_layer_norm is not None:
												__lowercase					     =				xmod_layer.adapter_layer_norm.weight
												__lowercase					     =				xmod_layer.adapter_layer_norm.bias

								if sorted(bert_output.adapter_modules.keys()				) != sorted(xmod_layer.adapter_modules.keys()				):
												raise AssertionError('''Lists of language adapters do not match.'''				)
								for lang_code, adapter in xmod_layer.adapter_modules.items():
												__lowercase					     =				bert_output.adapter_modules[lang_code]
												__lowercase					     =				xmod_layer.adapter_modules[lang_code]
												__lowercase					     =				from_adapter.fca.weight
												__lowercase					     =				from_adapter.fca.bias
												__lowercase					     =				from_adapter.fca.weight
												__lowercase					     =				from_adapter.fca.bias

								# end of layer

				if xmod_sent_encoder.layer_norm is not None:
								__lowercase					     =				xmod_sent_encoder.layer_norm.weight
								__lowercase					     =				xmod_sent_encoder.layer_norm.bias

				if classification_head:
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].dense.weight
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].dense.bias
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].out_proj.weight
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								__lowercase					     =				xmod.model.encoder.lm_head.dense.weight
								__lowercase					     =				xmod.model.encoder.lm_head.dense.bias
								__lowercase					     =				xmod.model.encoder.lm_head.layer_norm.weight
								__lowercase					     =				xmod.model.encoder.lm_head.layer_norm.bias
								__lowercase					     =				xmod.model.encoder.lm_head.weight
								__lowercase					     =				xmod.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				__lowercase					     =				xmod.encode(lowercase				).unsqueeze(0				)  # batch of size 1
				model.roberta.set_default_language(lowercase				)

				__lowercase					     =				model(lowercase				)[0]
				if classification_head:
								__lowercase					     =				xmod.model.classification_heads['''mnli'''](xmod.extract_features(lowercase				)				)
				else:
								__lowercase					     =				xmod.model(lowercase				,     lang_id=[SAMPLE_LANGUAGE]				)[0]
				print(our_output.shape				,     their_output.shape				)
				__lowercase					     =				torch.max(torch.abs(our_output - their_output				)				).item()
				print(F"max_absolute_diff = {max_absolute_diff}"				)  # ~ 1e-7
				__lowercase					     =				torch.allclose(lowercase				,     lowercase				,     atol=1E-3				)
				print('''Do both models output the same tensors?'''				,     '''🔥''' if success else '''💩'''				)
				if not success:
								raise Exception('''Something went wRoNg'''				)

				Path(lowercase				).mkdir(parents=lowercase				,     exist_ok=lowercase				)
				print(F"Saving model to {pytorch_dump_folder_path}"				)
				model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	List[str]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		parser.add_argument(
		    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
		)
		__a    :	List[str]         =    parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)